The Space Time Pattern Mining toolbox contains statistical tools for analyzing data distributions and patterns in the context of both space and time. It includes a toolset that can be helpful for visualizing the data stored in the space-time netCDF cube in both 2D and 3D and filling missing values in your data prior to cube creation.
Create Space Time Cube By Aggregating Points and Create Space Time Cube From Defined Locations take datasets and build a multidimensional cube data structure (netCDF) for analysis. Emerging Hot Spot Analysis then takes the cube as input and identifies statistically significant hot and cold spot trends over time. You might use the Emerging Hot Spot Analysis tool to analyze crime or disease outbreak data to locate new, intensifying, persistent, or sporadic hot spot patterns at different time-step intervals. The Local Outlier Analysis tool takes the cube as input to identify statistically significant clusters of high or low values as well as outliers that have values that are statistically different than their neighbors in space and time. The Utilities toolset enables you to estimate any missing values that may be present in your original dataset as well as visualize the data and analysis results stored in the space-time cube in two and three dimensions. These visualization tools can be used to understand the structure of the cube, how the cube aggregation process works, and to visualize the analytical results added to the cube by other Space Time Pattern Mining tools. See Visualizing the Space Time Cube for strategies to allow you to look at cube contents.
Summarizes a set of points into a netCDF data structure by aggregating them into space-time bins. Within each bin, the points are counted and specified attributes are aggregated. For all bin locations, the trend for counts and summarized attributes are evaluated.
Creates a netCDF data structure from panel data, station data, or other data where the locations are fixed and attributes change over time. For all locations, the trends for attributes are evaluated.
Identifies trends in the clustering of point counts or attributes in a netCDF space-time cube. Categories include new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating, and historical hot and cold spots.
Identifies statistically significant clusters of high or low values as well as outliers that have values that are statistically different than their neighbors in space and time.
This toolset contains tools for visualizing the variables stored in a netCDF cube.
www.esriurl.com/SpatialStats contains an up-to-date list of all of the resources available for using the Spatial Statistics tools, including the following:
- Free web seminars
- Books, articles, and white papers
- Sample scripts and case studies